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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# pragma pylint: disable=missing-docstring, C0103
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from datetime import timezone
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import pandas as pd
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from numpy import nan
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from pandas import DataFrame, Timestamp
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from freqtrade.data.btanalysis.historic_precision import get_tick_size_over_time
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def test_get_tick_size_over_time():
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"""
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Test the get_tick_size_over_time function with predefined data
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"""
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# Create test dataframe with different levels of precision
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data = {
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"date": [
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Timestamp("2020-01-01 00:00:00", tz=timezone.utc),
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Timestamp("2020-01-02 00:00:00", tz=timezone.utc),
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Timestamp("2020-01-03 00:00:00", tz=timezone.utc),
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Timestamp("2020-01-15 00:00:00", tz=timezone.utc),
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Timestamp("2020-01-16 00:00:00", tz=timezone.utc),
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Timestamp("2020-01-31 00:00:00", tz=timezone.utc),
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Timestamp("2020-02-01 00:00:00", tz=timezone.utc),
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Timestamp("2020-02-15 00:00:00", tz=timezone.utc),
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Timestamp("2020-03-15 00:00:00", tz=timezone.utc),
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],
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"open": [1.23456, 1.234, 1.23, 1.2, 1.23456, 1.234, 2.3456, 2.34, 2.34],
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"high": [1.23457, 1.235, 1.24, 1.3, 1.23456, 1.235, 2.3457, 2.34, 2.34],
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"low": [1.23455, 1.233, 1.22, 1.1, 1.23456, 1.233, 2.3455, 2.34, 2.34],
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"close": [1.23456, 1.234, 1.23, 1.2, 1.23456, 1.234, 2.3456, 2.34, 2.34],
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"volume": [100, 200, 300, 400, 500, 600, 700, 800, 900],
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}
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candles = DataFrame(data)
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# Calculate significant digits
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result = get_tick_size_over_time(candles)
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# Check that the result is a pandas Series
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assert isinstance(result, pd.Series)
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# Check that we have three months of data (Jan, Feb and March 2020 )
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assert len(result) == 3
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# Before
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assert result.asof("2019-01-01 00:00:00+00:00") is nan
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# January should have 5 significant digits (based on 1.23456789 being the most precise value)
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# which should be converted to 0.00001
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assert result.asof("2020-01-01 00:00:00+00:00") == 0.00001
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assert result.asof("2020-01-01 00:00:00+00:00") == 0.00001
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assert result.asof("2020-02-25 00:00:00+00:00") == 0.0001
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assert result.asof("2020-03-25 00:00:00+00:00") == 0.01
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assert result.asof("2020-04-01 00:00:00+00:00") == 0.01
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# Value far past the last date should be the last value
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assert result.asof("2025-04-01 00:00:00+00:00") == 0.01
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assert result.iloc[0] == 0.00001
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def test_get_tick_size_over_time_real_data(testdatadir):
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"""
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Test the get_tick_size_over_time function with real data from the testdatadir
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"""
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from freqtrade.data.history import load_pair_history
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# Load some test data from the testdata directory
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pair = "UNITTEST/BTC"
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timeframe = "1m"
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candles = load_pair_history(
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datadir=testdatadir,
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pair=pair,
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timeframe=timeframe,
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)
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# Make sure we have test data
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assert not candles.empty, "No test data found, cannot run test"
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# Calculate significant digits
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result = get_tick_size_over_time(candles)
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assert isinstance(result, pd.Series)
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# Verify that all values are between 0 and 1 (valid precision values)
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assert all(result > 0)
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assert all(result < 1)
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assert all(result <= 0.0001)
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assert all(result >= 0.00000001)
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